When assessing a statistical model, what should be considered if extreme values are present?

When assessing a statistical model in the presence of extreme values, considering the strength of the model is crucial. Extreme values, or outliers, can disproportionately influence the results of a statistical analysis, potentially altering correlation coefficients, regression lines, and any conclusions drawn from the model.

A strong model should be robust to such extreme values, ensuring that it generalizes well to a broader dataset. It’s essential to understand whether these extreme values are affecting the model’s ability to fit the data accurately. If an outlier significantly impacts the model, it may indicate that the model is sensitive to those extremes, which can reduce its reliability in making predictions or interpretations based on the rest of the data.

Other aspects, such as the validity of the analysis or the potential classification of values as outliers, could be relevant, but the primary concern should be whether the model's strength is compromised by these extreme values. A strong model will maintain its integrity and provide reliable insights, even when facing unusual data points, making the evaluation of the model’s strength in such scenarios especially important.